Query-Driven Visualization (QDV) is a knowledge discovery strategy that combines state-of-the-art methods from scientific data management with modern visualization approaches to support rapid data analysis. By restricting computational and cognitive workload in visualization and interpretation to records defined to be significant by a scientist, fast visualization responses can answer intuitive questions about the data. Thus, query-driven techniques are ideal tools for data exploration and hypothesis testing. However, uncertainty in data and query can strongly and negatively influence a visualization result and hence the insight obtained from it. This research project explores a novel framework that generalizes QDV and is aimed at addressing deficiencies in existing methods.
The approach to providing robust visualization techniques that incorporate the uncertain nature of the analysis process into the visualization result, thus enabling tools that will allow users to factor uncertainty into the conclusions drawn from data sets are based on modeling uncertainty at all levels of the query-driven process. The methods developed leverage multi-resolution data representations incorporating uncertainty information; and this results in improved efficiency and parallel computation in answering queries over very large, high-dimensional data sets. New visualization techniques are derived by taking advantage of the improved flexibility, generality and efficiency of the provided framework, to address specifically the needs of comparative visualization of an ensemble of data sets pertaining to a common science problem.
The resulting robust query-driven visualization techniques that incorporate the uncertain nature of the analysis in the knowledge discovery process will allow users to factor uncertainty into the conclusions drawn from complex, large-scale, high-dimensional data sets. In order to increase the impact of the research, results will be accessible via the project Web site (http://idav.ucdavis.edu/~joy/NSF-IIS-1018097.html), and incorporated into an open-source visualization package. Project provides research experience to students.
